Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

We introduce contextual stochastic bilevel optimization (CSBO)--a stochastic bilevel
optimization framework with the lower-level problem minimizing an expectation conditioned …

We consider stochastic optimization with side information where, prior to decision-making,
covariate data are available to inform better decisions. To hedge against data uncertainty …

Overfitting commonly occurs when applying deep neural networks (DNNs) on small-scale
datasets, where DNNs do not generalize well from existing data to unseen data. The main …

This paper focuses on the contextual optimization problem where a decision is subject to
some uncertain parameters and covariates that have some predictive power on those …

Data-driven optimization often involves the prediction of uncertain parameters drawn from
unknown probability distributions for a subsequent optimization task. Recent literature has …

This paper introduces an end-to-end online portfolio decision model within the framework of
direct reinforcement learning, seamlessly integrating the multi-factor model and mean …

The global energy situation is shifting towards renewable energy sources (RESs) to promote
sustainability and reduce fossil fuel reliance. This shift brings uncertainties from volatile …

The recently-proposed framework of Predict+ Optimize tackles optimization problems with
parameters that are unknown at solving time, in a supervised learning setting. Prior …